Overview

Dataset statistics

Number of variables44
Number of observations125972
Missing cells1047
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.3 MiB
Average record size in memory352.0 B

Variable types

Numeric29
Categorical15

Alerts

num_outbound_cmds has constant value "0" Constant
service has a high cardinality: 70 distinct values High cardinality
duration is highly correlated with dst_host_diff_srv_rateHigh correlation
src_bytes is highly correlated with dst_bytes and 8 other fieldsHigh correlation
dst_bytes is highly correlated with src_bytes and 7 other fieldsHigh correlation
hot is highly correlated with service and 1 other fieldsHigh correlation
num_failed_logins is highly correlated with attackHigh correlation
num_compromised is highly correlated with num_root and 1 other fieldsHigh correlation
num_root is highly correlated with num_compromised and 1 other fieldsHigh correlation
num_file_creations is highly correlated with num_outbound_cmdsHigh correlation
num_access_files is highly correlated with num_compromised and 3 other fieldsHigh correlation
count is highly correlated with service and 13 other fieldsHigh correlation
srv_count is highly correlated with protocol_type and 3 other fieldsHigh correlation
serror_rate is highly correlated with service and 12 other fieldsHigh correlation
srv_serror_rate is highly correlated with service and 12 other fieldsHigh correlation
rerror_rate is highly correlated with flag and 8 other fieldsHigh correlation
srv_rerror_rate is highly correlated with flag and 5 other fieldsHigh correlation
same_srv_rate is highly correlated with service and 13 other fieldsHigh correlation
diff_srv_rate is highly correlated with rerror_rate and 3 other fieldsHigh correlation
srv_diff_host_rate is highly correlated with service and 2 other fieldsHigh correlation
dst_host_count is highly correlated with service and 8 other fieldsHigh correlation
dst_host_srv_count is highly correlated with service and 12 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with service and 13 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with duration and 7 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with protocol_type and 8 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with protocol_type and 7 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with service and 13 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with service and 11 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with flag and 6 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with flag and 3 other fieldsHigh correlation
last_flag is highly correlated with service and 3 other fieldsHigh correlation
protocol_type is highly correlated with service and 5 other fieldsHigh correlation
service is highly correlated with protocol_type and 21 other fieldsHigh correlation
flag is highly correlated with service and 15 other fieldsHigh correlation
land is highly correlated with attackHigh correlation
wrong_fragment is highly correlated with attackHigh correlation
urgent is highly correlated with num_outbound_cmdsHigh correlation
logged_in is highly correlated with service and 11 other fieldsHigh correlation
root_shell is highly correlated with num_access_filesHigh correlation
su_attempted is highly correlated with num_access_filesHigh correlation
num_shells is highly correlated with attackHigh correlation
num_outbound_cmds is highly correlated with su_attempted and 13 other fieldsHigh correlation
is_host_login is highly correlated with num_outbound_cmdsHigh correlation
is_guest_login is highly correlated with service and 1 other fieldsHigh correlation
attack is highly correlated with protocol_type and 27 other fieldsHigh correlation
attack_class is highly correlated with protocol_type and 16 other fieldsHigh correlation
src_bytes is highly skewed (γ1 = 190.6685901) Skewed
dst_bytes is highly skewed (γ1 = 290.0517595) Skewed
num_failed_logins is highly skewed (γ1 = 53.76421073) Skewed
num_compromised is highly skewed (γ1 = 250.1068908) Skewed
num_root is highly skewed (γ1 = 236.9127838) Skewed
num_file_creations is highly skewed (γ1 = 55.66511972) Skewed
num_access_files is highly skewed (γ1 = 45.55478025) Skewed
duration has 115954 (92.0%) zeros Zeros
src_bytes has 49392 (39.2%) zeros Zeros
dst_bytes has 67966 (54.0%) zeros Zeros
hot has 123301 (97.9%) zeros Zeros
num_failed_logins has 125850 (99.9%) zeros Zeros
num_compromised has 124686 (99.0%) zeros Zeros
num_root has 125323 (99.5%) zeros Zeros
num_file_creations has 125685 (99.8%) zeros Zeros
num_access_files has 125601 (99.7%) zeros Zeros
serror_rate has 86828 (68.9%) zeros Zeros
srv_serror_rate has 88753 (70.5%) zeros Zeros
rerror_rate has 109782 (87.1%) zeros Zeros
srv_rerror_rate has 109766 (87.1%) zeros Zeros
same_srv_rate has 2766 (2.2%) zeros Zeros
diff_srv_rate has 76216 (60.5%) zeros Zeros
srv_diff_host_rate has 97573 (77.5%) zeros Zeros
dst_host_same_srv_rate has 6927 (5.5%) zeros Zeros
dst_host_diff_srv_rate has 46989 (37.3%) zeros Zeros
dst_host_same_src_port_rate has 63023 (50.0%) zeros Zeros
dst_host_srv_diff_host_rate has 86903 (69.0%) zeros Zeros
dst_host_serror_rate has 81385 (64.6%) zeros Zeros
dst_host_srv_serror_rate has 85359 (67.8%) zeros Zeros
dst_host_rerror_rate has 103178 (81.9%) zeros Zeros
dst_host_srv_rerror_rate has 106615 (84.6%) zeros Zeros

Reproduction

Analysis started2022-11-06 19:04:49.366183
Analysis finished2022-11-06 19:07:49.974836
Duration3 minutes and 0.61 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

duration
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2981
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.1469295
Minimum0
Maximum42908
Zeros115954
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:50.114865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum42908
Range42908
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2604.525522
Coefficient of variation (CV)9.070358254
Kurtosis156.0755362
Mean287.1469295
Median Absolute Deviation (MAD)0
Skewness11.8801818
Sum36172473
Variance6783553.194
MonotonicityNot monotonic
2022-11-07T00:37:50.220858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0115954
92.0%
11989
 
1.6%
2843
 
0.7%
3557
 
0.4%
4351
 
0.3%
5298
 
0.2%
27197
 
0.2%
6193
 
0.2%
28181
 
0.1%
7127
 
0.1%
Other values (2971)5282
 
4.2%
ValueCountFrequency (%)
0115954
92.0%
11989
 
1.6%
2843
 
0.7%
3557
 
0.4%
4351
 
0.3%
5298
 
0.2%
6193
 
0.2%
7127
 
0.1%
898
 
0.1%
995
 
0.1%
ValueCountFrequency (%)
429081
< 0.1%
428881
< 0.1%
428621
< 0.1%
428371
< 0.1%
428041
< 0.1%
427781
< 0.1%
427461
< 0.1%
427231
< 0.1%
426991
< 0.1%
426791
< 0.1%

protocol_type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
tcp
102688 
udp
14993 
icmp
 
8291

Length

Max length4
Median length3
Mean length3.065816213
Min length3

Characters and Unicode

Total characters386207
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowudp
2nd rowtcp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp102688
81.5%
udp14993
 
11.9%
icmp8291
 
6.6%

Length

2022-11-07T00:37:50.322839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:50.424891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
tcp102688
81.5%
udp14993
 
11.9%
icmp8291
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p125972
32.6%
c110979
28.7%
t102688
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter386207
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p125972
32.6%
c110979
28.7%
t102688
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin386207
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p125972
32.6%
c110979
28.7%
t102688
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII386207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p125972
32.6%
c110979
28.7%
t102688
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

service
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
http
40338 
private
21853 
domain_u
9043 
smtp
7313 
ftp_data
6859 
Other values (65)
40566 

Length

Max length11
Median length10
Mean length5.466429048
Min length3

Characters and Unicode

Total characters688617
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowother
2nd rowprivate
3rd rowhttp
4th rowhttp
5th rowprivate

Common Values

ValueCountFrequency (%)
http40338
32.0%
private21853
17.3%
domain_u9043
 
7.2%
smtp7313
 
5.8%
ftp_data6859
 
5.4%
eco_i4586
 
3.6%
other4359
 
3.5%
ecr_i3077
 
2.4%
telnet2353
 
1.9%
finger1767
 
1.4%
Other values (60)24424
19.4%

Length

2022-11-07T00:37:50.517838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http40338
32.0%
private21853
17.3%
domain_u9043
 
7.2%
smtp7313
 
5.8%
ftp_data6859
 
5.4%
eco_i4586
 
3.6%
other4359
 
3.5%
ecr_i3077
 
2.4%
telnet2353
 
1.9%
finger1767
 
1.4%
Other values (60)24424
19.4%

Most occurring characters

ValueCountFrequency (%)
t145595
21.1%
p88150
12.8%
a51382
 
7.5%
h49666
 
7.2%
e49119
 
7.1%
i48525
 
7.0%
r34885
 
5.1%
_29464
 
4.3%
o24559
 
3.6%
n22585
 
3.3%
Other values (30)144687
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter651472
94.6%
Connector Punctuation29464
 
4.3%
Decimal Number6185
 
0.9%
Uppercase Letter1496
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t145595
22.3%
p88150
13.5%
a51382
 
7.9%
h49666
 
7.6%
e49119
 
7.5%
i48525
 
7.4%
r34885
 
5.4%
o24559
 
3.8%
n22585
 
3.5%
v22472
 
3.4%
Other values (15)114534
17.6%
Decimal Number
ValueCountFrequency (%)
41708
27.6%
31656
26.8%
0866
14.0%
5862
13.9%
9862
13.9%
1148
 
2.4%
279
 
1.3%
83
 
< 0.1%
71
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z862
57.6%
C187
 
12.5%
I187
 
12.5%
R187
 
12.5%
X73
 
4.9%
Connector Punctuation
ValueCountFrequency (%)
_29464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin652968
94.8%
Common35649
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t145595
22.3%
p88150
13.5%
a51382
 
7.9%
h49666
 
7.6%
e49119
 
7.5%
i48525
 
7.4%
r34885
 
5.3%
o24559
 
3.8%
n22585
 
3.5%
v22472
 
3.4%
Other values (20)116030
17.8%
Common
ValueCountFrequency (%)
_29464
82.7%
41708
 
4.8%
31656
 
4.6%
0866
 
2.4%
5862
 
2.4%
9862
 
2.4%
1148
 
0.4%
279
 
0.2%
83
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII688617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t145595
21.1%
p88150
12.8%
a51382
 
7.5%
h49666
 
7.2%
e49119
 
7.1%
i48525
 
7.0%
r34885
 
5.1%
_29464
 
4.3%
o24559
 
3.6%
n22585
 
3.3%
Other values (30)144687
21.0%

flag
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
SF
74944 
S0
34851 
REJ
11233 
RSTR
 
2421
RSTO
 
1562
Other values (6)
 
961

Length

Max length6
Median length2
Mean length2.156042613
Min length2

Characters and Unicode

Total characters271601
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowS0
3rd rowSF
4th rowSF
5th rowREJ

Common Values

ValueCountFrequency (%)
SF74944
59.5%
S034851
27.7%
REJ11233
 
8.9%
RSTR2421
 
1.9%
RSTO1562
 
1.2%
S1365
 
0.3%
SH271
 
0.2%
S2127
 
0.1%
RSTOS0103
 
0.1%
S349
 
< 0.1%

Length

2022-11-07T00:37:50.612847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf74944
59.5%
s034851
27.7%
rej11233
 
8.9%
rstr2421
 
1.9%
rsto1562
 
1.2%
s1365
 
0.3%
sh271
 
0.2%
s2127
 
0.1%
rstos0103
 
0.1%
s349
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S114796
42.3%
F74944
27.6%
034954
 
12.9%
R17740
 
6.5%
E11233
 
4.1%
J11233
 
4.1%
T4132
 
1.5%
O1711
 
0.6%
1365
 
0.1%
H317
 
0.1%
Other values (2)176
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter236106
86.9%
Decimal Number35495
 
13.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S114796
48.6%
F74944
31.7%
R17740
 
7.5%
E11233
 
4.8%
J11233
 
4.8%
T4132
 
1.8%
O1711
 
0.7%
H317
 
0.1%
Decimal Number
ValueCountFrequency (%)
034954
98.5%
1365
 
1.0%
2127
 
0.4%
349
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin236106
86.9%
Common35495
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S114796
48.6%
F74944
31.7%
R17740
 
7.5%
E11233
 
4.8%
J11233
 
4.8%
T4132
 
1.8%
O1711
 
0.7%
H317
 
0.1%
Common
ValueCountFrequency (%)
034954
98.5%
1365
 
1.0%
2127
 
0.4%
349
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII271601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S114796
42.3%
F74944
27.6%
034954
 
12.9%
R17740
 
6.5%
E11233
 
4.1%
J11233
 
4.1%
T4132
 
1.5%
O1711
 
0.6%
1365
 
0.1%
H317
 
0.1%
Other values (2)176
 
0.1%

src_bytes
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3341
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45567.10082
Minimum0
Maximum1379963888
Zeros49392
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:50.711839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3276
95-th percentile1480
Maximum1379963888
Range1379963888
Interquartile range (IQR)276

Descriptive statistics

Standard deviation5870354.481
Coefficient of variation (CV)128.8287904
Kurtosis39353.80883
Mean45567.10082
Median Absolute Deviation (MAD)44
Skewness190.6685901
Sum5740178825
Variance3.446106173 × 1013
MonotonicityNot monotonic
2022-11-07T00:37:50.823869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049392
39.2%
83691
 
2.9%
12432
 
1.9%
442334
 
1.9%
452089
 
1.7%
10322001
 
1.6%
461294
 
1.0%
431284
 
1.0%
105998
 
0.8%
147948
 
0.8%
Other values (3331)59509
47.2%
ValueCountFrequency (%)
049392
39.2%
12432
 
1.9%
42
 
< 0.1%
528
 
< 0.1%
6147
 
0.1%
7107
 
0.1%
83691
 
2.9%
9199
 
0.2%
10195
 
0.2%
1176
 
0.1%
ValueCountFrequency (%)
13799638881
< 0.1%
11675194971
< 0.1%
6933756401
< 0.1%
6215686631
< 0.1%
3817090901
< 0.1%
2172773391
< 0.1%
895815201
< 0.1%
244187761
< 0.1%
219455201
< 0.1%
188289761
< 0.1%

dst_bytes
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct9326
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19779.27143
Minimum0
Maximum1309937401
Zeros67966
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:50.934838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3516
95-th percentile8314
Maximum1309937401
Range1309937401
Interquartile range (IQR)516

Descriptive statistics

Standard deviation4021285.112
Coefficient of variation (CV)203.3080503
Kurtosis90941.01261
Mean19779.27143
Median Absolute Deviation (MAD)0
Skewness290.0517595
Sum2491634381
Variance1.617073395 × 1013
MonotonicityNot monotonic
2022-11-07T00:37:51.048836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067966
54.0%
1051497
 
1.2%
8314888
 
0.7%
330528
 
0.4%
331512
 
0.4%
44511
 
0.4%
42478
 
0.4%
328470
 
0.4%
332469
 
0.4%
4454
 
0.4%
Other values (9316)52199
41.4%
ValueCountFrequency (%)
067966
54.0%
122
 
< 0.1%
31
 
< 0.1%
4454
 
0.4%
54
 
< 0.1%
61
 
< 0.1%
121
 
< 0.1%
141
 
< 0.1%
1547
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
13099374011
< 0.1%
4002910602
< 0.1%
70286521
< 0.1%
51554681
< 0.1%
51537711
< 0.1%
51534601
< 0.1%
51513851
< 0.1%
51511541
< 0.1%
51510491
< 0.1%
51509381
< 0.1%

land
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125947 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125947
> 99.9%
125
 
< 0.1%

Length

2022-11-07T00:37:51.160902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:51.240897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0125947
> 99.9%
125
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0125947
> 99.9%
125
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125947
> 99.9%
125
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125947
> 99.9%
125
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125947
> 99.9%
125
 
< 0.1%

wrong_fragment
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
124882 
3
 
884
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0124882
99.1%
3884
 
0.7%
1206
 
0.2%

Length

2022-11-07T00:37:51.317838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:51.403895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0124882
99.1%
3884
 
0.7%
1206
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0124882
99.1%
3884
 
0.7%
1206
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0124882
99.1%
3884
 
0.7%
1206
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0124882
99.1%
3884
 
0.7%
1206
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0124882
99.1%
3884
 
0.7%
1206
 
0.2%

urgent
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125963 
1
 
5
2
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125963
> 99.9%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

Length

2022-11-07T00:37:51.481909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:51.564887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0125963
> 99.9%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0125963
> 99.9%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125963
> 99.9%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125963
> 99.9%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125963
> 99.9%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

hot
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2044105039
Minimum0
Maximum77
Zeros123301
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:51.641915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.14997689
Coefficient of variation (CV)10.51793743
Kurtosis168.0128936
Mean0.2044105039
Median Absolute Deviation (MAD)0
Skewness12.58983526
Sum25750
Variance4.622400628
MonotonicityNot monotonic
2022-11-07T00:37:51.739835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0123301
97.9%
21037
 
0.8%
1369
 
0.3%
28277
 
0.2%
30256
 
0.2%
4173
 
0.1%
6140
 
0.1%
576
 
0.1%
2468
 
0.1%
1957
 
< 0.1%
Other values (18)218
 
0.2%
ValueCountFrequency (%)
0123301
97.9%
1369
 
0.3%
21037
 
0.8%
354
 
< 0.1%
4173
 
0.1%
576
 
0.1%
6140
 
0.1%
75
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
771
 
< 0.1%
442
 
< 0.1%
331
 
< 0.1%
30256
0.2%
28277
0.2%
252
 
< 0.1%
2468
 
0.1%
2255
 
< 0.1%
211
 
< 0.1%
209
 
< 0.1%

num_failed_logins
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001222493888
Minimum0
Maximum5
Zeros125850
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:51.826866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04523931841
Coefficient of variation (CV)37.00576246
Kurtosis3869.038558
Mean0.001222493888
Median Absolute Deviation (MAD)0
Skewness53.76421073
Sum154
Variance0.00204659593
MonotonicityNot monotonic
2022-11-07T00:37:51.900904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0125850
99.9%
1104
 
0.1%
29
 
< 0.1%
35
 
< 0.1%
43
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
0125850
99.9%
1104
 
0.1%
29
 
< 0.1%
35
 
< 0.1%
43
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
43
 
< 0.1%
35
 
< 0.1%
29
 
< 0.1%
1104
 
0.1%
0125850
99.9%

logged_in
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
76120 
1
49852 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
076120
60.4%
149852
39.6%

Length

2022-11-07T00:37:51.983837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:52.066837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
076120
60.4%
149852
39.6%

Most occurring characters

ValueCountFrequency (%)
076120
60.4%
149852
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
076120
60.4%
149852
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
076120
60.4%
149852
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
076120
60.4%
149852
39.6%

num_compromised
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2792525323
Minimum0
Maximum7479
Zeros124686
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:52.151870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7479
Range7479
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.94213726
Coefficient of variation (CV)85.73650904
Kurtosis75955.62481
Mean0.2792525323
Median Absolute Deviation (MAD)0
Skewness250.1068908
Sum35178
Variance573.2259366
MonotonicityNot monotonic
2022-11-07T00:37:52.259906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0124686
99.0%
1976
 
0.8%
298
 
0.1%
440
 
< 0.1%
338
 
< 0.1%
619
 
< 0.1%
517
 
< 0.1%
75
 
< 0.1%
83
 
< 0.1%
93
 
< 0.1%
Other values (78)87
 
0.1%
ValueCountFrequency (%)
0124686
99.0%
1976
 
0.8%
298
 
0.1%
338
 
< 0.1%
440
 
< 0.1%
517
 
< 0.1%
619
 
< 0.1%
75
 
< 0.1%
83
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
74791
< 0.1%
17391
< 0.1%
10431
< 0.1%
8842
< 0.1%
8091
< 0.1%
7891
< 0.1%
7671
< 0.1%
7611
< 0.1%
7561
< 0.1%
7511
< 0.1%

root_shell
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125803 
1
 
169

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125803
99.9%
1169
 
0.1%

Length

2022-11-07T00:37:52.363884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:52.450860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0125803
99.9%
1169
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0125803
99.9%
1169
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125803
99.9%
1169
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125803
99.9%
1169
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125803
99.9%
1169
 
0.1%

su_attempted
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125892 
2
 
59
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125892
99.9%
259
 
< 0.1%
121
 
< 0.1%

Length

2022-11-07T00:37:52.523908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:52.608889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0125892
99.9%
259
 
< 0.1%
121
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0125892
99.9%
259
 
< 0.1%
121
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125892
99.9%
259
 
< 0.1%
121
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125892
99.9%
259
 
< 0.1%
121
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125892
99.9%
259
 
< 0.1%
121
 
< 0.1%

num_root
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3021941384
Minimum0
Maximum7468
Zeros125323
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:52.695901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7468
Range7468
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.39971492
Coefficient of variation (CV)80.74185373
Kurtosis70069.6526
Mean0.3021941384
Median Absolute Deviation (MAD)0
Skewness236.9127838
Sum38068
Variance595.3460882
MonotonicityNot monotonic
2022-11-07T00:37:52.809885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0125323
99.5%
1273
 
0.2%
9121
 
0.1%
699
 
0.1%
233
 
< 0.1%
524
 
< 0.1%
412
 
< 0.1%
37
 
< 0.1%
72
 
< 0.1%
8572
 
< 0.1%
Other values (72)76
 
0.1%
ValueCountFrequency (%)
0125323
99.5%
1273
 
0.2%
233
 
< 0.1%
37
 
< 0.1%
412
 
< 0.1%
524
 
< 0.1%
699
 
0.1%
72
 
< 0.1%
81
 
< 0.1%
9121
 
0.1%
ValueCountFrequency (%)
74681
< 0.1%
17431
< 0.1%
10451
< 0.1%
9931
< 0.1%
9751
< 0.1%
8891
< 0.1%
8671
< 0.1%
8572
< 0.1%
8491
< 0.1%
8411
< 0.1%

num_file_creations
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01266948211
Minimum0
Maximum43
Zeros125685
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:52.920841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4839369889
Coefficient of variation (CV)38.19706163
Kurtosis3603.28308
Mean0.01266948211
Median Absolute Deviation (MAD)0
Skewness55.66511972
Sum1596
Variance0.2341950092
MonotonicityNot monotonic
2022-11-07T00:37:53.013889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0125685
99.8%
1151
 
0.1%
241
 
< 0.1%
413
 
< 0.1%
35
 
< 0.1%
85
 
< 0.1%
155
 
< 0.1%
105
 
< 0.1%
55
 
< 0.1%
175
 
< 0.1%
Other values (25)52
 
< 0.1%
ValueCountFrequency (%)
0125685
99.8%
1151
 
0.1%
241
 
< 0.1%
35
 
< 0.1%
413
 
< 0.1%
55
 
< 0.1%
63
 
< 0.1%
74
 
< 0.1%
85
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
431
 
< 0.1%
403
< 0.1%
381
 
< 0.1%
361
 
< 0.1%
341
 
< 0.1%
331
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
263
< 0.1%

num_shells
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125925 
1
 
42
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125925
> 99.9%
142
 
< 0.1%
25
 
< 0.1%

Length

2022-11-07T00:37:53.109883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:53.196887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0125925
> 99.9%
142
 
< 0.1%
25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0125925
> 99.9%
142
 
< 0.1%
25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125925
> 99.9%
142
 
< 0.1%
25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125925
> 99.9%
142
 
< 0.1%
25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125925
> 99.9%
142
 
< 0.1%
25
 
< 0.1%

num_access_files
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00409614835
Minimum0
Maximum9
Zeros125601
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:53.271882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09936994949
Coefficient of variation (CV)24.25936294
Kurtosis2862.781626
Mean0.00409614835
Median Absolute Deviation (MAD)0
Skewness45.55478025
Sum516
Variance0.009874386862
MonotonicityNot monotonic
2022-11-07T00:37:53.343882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0125601
99.7%
1313
 
0.2%
229
 
< 0.1%
38
 
< 0.1%
56
 
< 0.1%
45
 
< 0.1%
64
 
< 0.1%
83
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
0125601
99.7%
1313
 
0.2%
229
 
< 0.1%
38
 
< 0.1%
45
 
< 0.1%
56
 
< 0.1%
64
 
< 0.1%
72
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
83
 
< 0.1%
72
 
< 0.1%
64
 
< 0.1%
56
 
< 0.1%
45
 
< 0.1%
38
 
< 0.1%
229
 
< 0.1%
1313
 
0.2%
0125601
99.7%

num_outbound_cmds
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125972 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125972
100.0%

Length

2022-11-07T00:37:54.188883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:54.275888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0125972
100.0%

Most occurring characters

ValueCountFrequency (%)
0125972
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125972
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125972
100.0%

is_host_login
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125971 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125971
> 99.9%
11
 
< 0.1%

Length

2022-11-07T00:37:54.347852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:54.432838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0125971
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0125971
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125971
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125971
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125971
> 99.9%
11
 
< 0.1%

is_guest_login
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
124785 
1
 
1187

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125972
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0124785
99.1%
11187
 
0.9%

Length

2022-11-07T00:37:54.509882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:54.595887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0124785
99.1%
11187
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0124785
99.1%
11187
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125972
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0124785
99.1%
11187
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common125972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0124785
99.1%
11187
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII125972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0124785
99.1%
11187
 
0.9%

count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct512
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.10820659
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:54.682838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median14
Q3143
95-th percentile286
Maximum511
Range511
Interquartile range (IQR)141

Descriptive statistics

Standard deviation114.5088282
Coefficient of variation (CV)1.361446556
Kurtosis2.006881181
Mean84.10820659
Median Absolute Deviation (MAD)13
Skewness1.514263629
Sum10595279
Variance13112.27173
MonotonicityNot monotonic
2022-11-07T00:37:54.795907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127763
22.0%
29473
 
7.5%
33962
 
3.1%
43550
 
2.8%
52980
 
2.4%
62413
 
1.9%
72325
 
1.8%
81902
 
1.5%
91712
 
1.4%
101610
 
1.3%
Other values (502)68282
54.2%
ValueCountFrequency (%)
013
 
< 0.1%
127763
22.0%
29473
 
7.5%
33962
 
3.1%
43550
 
2.8%
52980
 
2.4%
62413
 
1.9%
72325
 
1.8%
81902
 
1.5%
91712
 
1.4%
ValueCountFrequency (%)
5111437
1.1%
510307
 
0.2%
509243
 
0.2%
50831
 
< 0.1%
5076
 
< 0.1%
5063
 
< 0.1%
5052
 
< 0.1%
5043
 
< 0.1%
5034
 
< 0.1%
5025
 
< 0.1%

srv_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct509
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.73809259
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:54.909882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8
Q318
95-th percentile158
Maximum511
Range511
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.63609175
Coefficient of variation (CV)2.618640467
Kurtosis24.24426877
Mean27.73809259
Median Absolute Deviation (MAD)7
Skewness4.694142224
Sum3494223
Variance5276.001825
MonotonicityNot monotonic
2022-11-07T00:37:55.026893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125398
20.2%
212819
 
10.2%
36336
 
5.0%
45526
 
4.4%
54636
 
3.7%
64156
 
3.3%
73992
 
3.2%
83697
 
2.9%
93528
 
2.8%
113293
 
2.6%
Other values (499)52591
41.7%
ValueCountFrequency (%)
013
 
< 0.1%
125398
20.2%
212819
10.2%
36336
 
5.0%
45526
 
4.4%
54636
 
3.7%
64156
 
3.3%
73992
 
3.2%
83697
 
2.9%
93528
 
2.8%
ValueCountFrequency (%)
5111012
0.8%
510160
 
0.1%
50949
 
< 0.1%
50811
 
< 0.1%
5073
 
< 0.1%
5031
 
< 0.1%
5022
 
< 0.1%
5011
 
< 0.1%
5002
 
< 0.1%
4992
 
< 0.1%

serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2844867907
Minimum0
Maximum1
Zeros86828
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:55.138887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4464566769
Coefficient of variation (CV)1.569340621
Kurtosis-1.054627756
Mean0.2844867907
Median Absolute Deviation (MAD)0
Skewness0.9631882247
Sum35837.37
Variance0.1993235643
MonotonicityNot monotonic
2022-11-07T00:37:55.243838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086828
68.9%
134439
 
27.3%
0.5493
 
0.4%
0.33321
 
0.3%
0.07305
 
0.2%
0.06298
 
0.2%
0.08254
 
0.2%
0.99250
 
0.2%
0.01216
 
0.2%
0.25208
 
0.2%
Other values (79)2360
 
1.9%
ValueCountFrequency (%)
086828
68.9%
0.01216
 
0.2%
0.0284
 
0.1%
0.03150
 
0.1%
0.04131
 
0.1%
0.05192
 
0.2%
0.06298
 
0.2%
0.07305
 
0.2%
0.08254
 
0.2%
0.09189
 
0.2%
ValueCountFrequency (%)
134439
27.3%
0.99250
 
0.2%
0.9864
 
0.1%
0.9779
 
0.1%
0.9641
 
< 0.1%
0.9529
 
< 0.1%
0.9425
 
< 0.1%
0.9318
 
< 0.1%
0.9215
 
< 0.1%
0.918
 
< 0.1%

srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2824876163
Minimum0
Maximum1
Zeros88753
Zeros (%)70.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:55.352837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4470235641
Coefficient of variation (CV)1.582453666
Kurtosis-1.044317647
Mean0.2824876163
Median Absolute Deviation (MAD)0
Skewness0.9705849551
Sum35585.53
Variance0.1998300669
MonotonicityNot monotonic
2022-11-07T00:37:55.462905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088753
70.5%
134874
 
27.7%
0.5432
 
0.3%
0.33273
 
0.2%
0.25233
 
0.2%
0.2132
 
0.1%
0.17114
 
0.1%
0.0593
 
0.1%
0.0392
 
0.1%
0.0483
 
0.1%
Other values (76)893
 
0.7%
ValueCountFrequency (%)
088753
70.5%
0.016
 
< 0.1%
0.0260
 
< 0.1%
0.0392
 
0.1%
0.0483
 
0.1%
0.0593
 
0.1%
0.0665
 
0.1%
0.0767
 
0.1%
0.0863
 
0.1%
0.0944
 
< 0.1%
ValueCountFrequency (%)
134874
27.7%
0.961
 
< 0.1%
0.9540
 
< 0.1%
0.9413
 
< 0.1%
0.938
 
< 0.1%
0.9212
 
< 0.1%
0.9116
 
< 0.1%
0.910
 
< 0.1%
0.8912
 
< 0.1%
0.8811
 
< 0.1%

rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1199594354
Minimum0
Maximum1
Zeros109782
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:55.567900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3204366144
Coefficient of variation (CV)2.67120809
Kurtosis3.445782501
Mean0.1199594354
Median Absolute Deviation (MAD)0
Skewness2.325517754
Sum15111.53
Variance0.1026796238
MonotonicityNot monotonic
2022-11-07T00:37:55.676880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0109782
87.1%
112874
 
10.2%
0.9269
 
0.2%
0.92216
 
0.2%
0.93210
 
0.2%
0.89196
 
0.2%
0.91187
 
0.1%
0.5163
 
0.1%
0.88141
 
0.1%
0.95137
 
0.1%
Other values (72)1797
 
1.4%
ValueCountFrequency (%)
0109782
87.1%
0.0161
 
< 0.1%
0.0277
 
0.1%
0.0399
 
0.1%
0.0455
 
< 0.1%
0.0537
 
< 0.1%
0.0625
 
< 0.1%
0.0723
 
< 0.1%
0.0823
 
< 0.1%
0.0910
 
< 0.1%
ValueCountFrequency (%)
112874
10.2%
0.9923
 
< 0.1%
0.9817
 
< 0.1%
0.9732
 
< 0.1%
0.9672
 
0.1%
0.95137
 
0.1%
0.94121
 
0.1%
0.93210
 
0.2%
0.92216
 
0.2%
0.91187
 
0.1%

srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1211842314
Minimum0
Maximum1
Zeros109766
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:55.781917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3236483325
Coefficient of variation (CV)2.67071325
Kurtosis3.445769583
Mean0.1211842314
Median Absolute Deviation (MAD)0
Skewness2.327018991
Sum15265.82
Variance0.1047482431
MonotonicityNot monotonic
2022-11-07T00:37:55.893900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0109766
87.1%
114827
 
11.8%
0.5244
 
0.2%
0.33160
 
0.1%
0.25114
 
0.1%
0.292
 
0.1%
0.1773
 
0.1%
0.0450
 
< 0.1%
0.0347
 
< 0.1%
0.1445
 
< 0.1%
Other values (52)554
 
0.4%
ValueCountFrequency (%)
0109766
87.1%
0.013
 
< 0.1%
0.0240
 
< 0.1%
0.0347
 
< 0.1%
0.0450
 
< 0.1%
0.0542
 
< 0.1%
0.0633
 
< 0.1%
0.0727
 
< 0.1%
0.0840
 
< 0.1%
0.0924
 
< 0.1%
ValueCountFrequency (%)
114827
11.8%
0.962
 
< 0.1%
0.951
 
< 0.1%
0.922
 
< 0.1%
0.91
 
< 0.1%
0.893
 
< 0.1%
0.884
 
< 0.1%
0.873
 
< 0.1%
0.865
 
< 0.1%
0.8510
 
< 0.1%

same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6609249675
Minimum0
Maximum1
Zeros2766
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:56.004884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.4396235693
Coefficient of variation (CV)0.6651641124
Kurtosis-1.609780542
Mean0.6609249675
Median Absolute Deviation (MAD)0
Skewness-0.5724865372
Sum83258.04
Variance0.1932688827
MonotonicityNot monotonic
2022-11-07T00:37:56.117908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176811
61.0%
0.014027
 
3.2%
0.023616
 
2.9%
0.033503
 
2.8%
0.073438
 
2.7%
0.043227
 
2.6%
0.063220
 
2.6%
0.053088
 
2.5%
0.082815
 
2.2%
02766
 
2.2%
Other values (91)19461
 
15.4%
ValueCountFrequency (%)
02766
2.2%
0.014027
3.2%
0.023616
2.9%
0.033503
2.8%
0.043227
2.6%
0.053088
2.5%
0.063220
2.6%
0.073438
2.7%
0.082815
2.2%
0.091957
1.6%
ValueCountFrequency (%)
176811
61.0%
0.99759
 
0.6%
0.9897
 
0.1%
0.9744
 
< 0.1%
0.9616
 
< 0.1%
0.9514
 
< 0.1%
0.9421
 
< 0.1%
0.9333
 
< 0.1%
0.9243
 
< 0.1%
0.9125
 
< 0.1%

diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct95
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06305313879
Minimum0
Maximum1
Zeros76216
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:56.226903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.1803150357
Coefficient of variation (CV)2.859731318
Kurtosis18.89929202
Mean0.06305313879
Median Absolute Deviation (MAD)0
Skewness4.379796406
Sum7942.93
Variance0.0325135121
MonotonicityNot monotonic
2022-11-07T00:37:56.333898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
076216
60.5%
0.0618998
 
15.1%
0.079515
 
7.6%
0.056887
 
5.5%
13438
 
2.7%
0.081883
 
1.5%
0.011013
 
0.8%
0.09645
 
0.5%
0.04627
 
0.5%
0.5549
 
0.4%
Other values (85)6201
 
4.9%
ValueCountFrequency (%)
076216
60.5%
0.011013
 
0.8%
0.02264
 
0.2%
0.03282
 
0.2%
0.04627
 
0.5%
0.056887
 
5.5%
0.0618998
 
15.1%
0.079515
 
7.6%
0.081883
 
1.5%
0.09645
 
0.5%
ValueCountFrequency (%)
13438
2.7%
0.9939
 
< 0.1%
0.986
 
< 0.1%
0.977
 
< 0.1%
0.9629
 
< 0.1%
0.9539
 
< 0.1%
0.922
 
< 0.1%
0.911
 
< 0.1%
0.91
 
< 0.1%
0.891
 
< 0.1%

srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09732242086
Minimum0
Maximum1
Zeros97573
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:56.444882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2598313847
Coefficient of variation (CV)2.669799851
Kurtosis6.816217011
Mean0.09732242086
Median Absolute Deviation (MAD)0
Skewness2.86033944
Sum12259.9
Variance0.0675123485
MonotonicityNot monotonic
2022-11-07T00:37:56.547904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
097573
77.5%
18143
 
6.5%
0.012865
 
2.3%
0.5982
 
0.8%
0.67975
 
0.8%
0.12904
 
0.7%
0.33790
 
0.6%
0.02771
 
0.6%
0.11732
 
0.6%
0.25724
 
0.6%
Other values (50)11513
 
9.1%
ValueCountFrequency (%)
097573
77.5%
0.012865
 
2.3%
0.02771
 
0.6%
0.03218
 
0.2%
0.04187
 
0.1%
0.05325
 
0.3%
0.06520
 
0.4%
0.07519
 
0.4%
0.08653
 
0.5%
0.09618
 
0.5%
ValueCountFrequency (%)
18143
6.5%
0.881
 
< 0.1%
0.837
 
< 0.1%
0.860
 
< 0.1%
0.75235
 
0.2%
0.719
 
< 0.1%
0.67975
 
0.8%
0.627
 
< 0.1%
0.6178
 
0.1%
0.5733
 
< 0.1%

dst_host_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.1491998
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:56.649838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q182
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)173

Descriptive statistics

Standard deviation99.20656545
Coefficient of variation (CV)0.5446445307
Kurtosis-1.065776439
Mean182.1491998
Median Absolute Deviation (MAD)0
Skewness-0.8334428596
Sum22945699
Variance9841.942628
MonotonicityNot monotonic
2022-11-07T00:37:56.756894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25574099
58.8%
13119
 
2.5%
22733
 
2.2%
31280
 
1.0%
41198
 
1.0%
5723
 
0.6%
6701
 
0.6%
7645
 
0.5%
8595
 
0.5%
9578
 
0.5%
Other values (246)40301
32.0%
ValueCountFrequency (%)
03
 
< 0.1%
13119
2.5%
22733
2.2%
31280
1.0%
41198
 
1.0%
5723
 
0.6%
6701
 
0.6%
7645
 
0.5%
8595
 
0.5%
9578
 
0.5%
ValueCountFrequency (%)
25574099
58.8%
25470
 
0.1%
25389
 
0.1%
25277
 
0.1%
25190
 
0.1%
25093
 
0.1%
24978
 
0.1%
24887
 
0.1%
24789
 
0.1%
24683
 
0.1%

dst_host_srv_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.6537246
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:56.872912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median63
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)245

Descriptive statistics

Standard deviation110.7028855
Coefficient of variation (CV)0.9571925666
Kurtosis-1.756342466
Mean115.6537246
Median Absolute Deviation (MAD)61
Skewness0.2837071842
Sum14569131
Variance12255.12886
MonotonicityNot monotonic
2022-11-07T00:37:56.977894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25535993
28.6%
18449
 
6.7%
25161
 
4.1%
32768
 
2.2%
42488
 
2.0%
52336
 
1.9%
202300
 
1.8%
2542238
 
1.8%
62222
 
1.8%
192190
 
1.7%
Other values (246)59827
47.5%
ValueCountFrequency (%)
03
 
< 0.1%
18449
6.7%
25161
4.1%
32768
 
2.2%
42488
 
2.0%
52336
 
1.9%
62222
 
1.8%
72160
 
1.7%
82072
 
1.6%
91948
 
1.5%
ValueCountFrequency (%)
25535993
28.6%
2542238
 
1.8%
253472
 
0.4%
252213
 
0.2%
251402
 
0.3%
250302
 
0.2%
249248
 
0.2%
248205
 
0.2%
247220
 
0.2%
246196
 
0.2%

dst_host_same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5212444829
Minimum0
Maximum1
Zeros6927
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:57.089904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.51
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.4489500549
Coefficient of variation (CV)0.8613041858
Kurtosis-1.884046597
Mean0.5212444829
Median Absolute Deviation (MAD)0.49
Skewness-0.01046288591
Sum65662.21
Variance0.2015561518
MonotonicityNot monotonic
2022-11-07T00:37:57.192917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149059
38.9%
0.017780
 
6.2%
06927
 
5.5%
0.026593
 
5.2%
0.075672
 
4.5%
0.045208
 
4.1%
0.054951
 
3.9%
0.034049
 
3.2%
0.063444
 
2.7%
0.082816
 
2.2%
Other values (91)29473
23.4%
ValueCountFrequency (%)
06927
5.5%
0.017780
6.2%
0.026593
5.2%
0.034049
3.2%
0.045208
4.1%
0.054951
3.9%
0.063444
2.7%
0.075672
4.5%
0.082816
 
2.2%
0.091740
 
1.4%
ValueCountFrequency (%)
149059
38.9%
0.99688
 
0.5%
0.98821
 
0.7%
0.97478
 
0.4%
0.96675
 
0.5%
0.95580
 
0.5%
0.94393
 
0.3%
0.93457
 
0.4%
0.92341
 
0.3%
0.91395
 
0.3%

dst_host_diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08295152891
Minimum0
Maximum1
Zeros46989
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:57.298836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.07
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.1889224909
Coefficient of variation (CV)2.277504626
Kurtosis12.6342728
Mean0.08295152891
Median Absolute Deviation (MAD)0.02
Skewness3.609582931
Sum10449.57
Variance0.03569170755
MonotonicityNot monotonic
2022-11-07T00:37:57.407905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046989
37.3%
0.0716570
 
13.2%
0.069787
 
7.8%
0.019295
 
7.4%
0.057321
 
5.8%
0.087001
 
5.6%
0.026716
 
5.3%
0.033562
 
2.8%
0.043091
 
2.5%
0.092569
 
2.0%
Other values (91)13071
 
10.4%
ValueCountFrequency (%)
046989
37.3%
0.019295
 
7.4%
0.026716
 
5.3%
0.033562
 
2.8%
0.043091
 
2.5%
0.057321
 
5.8%
0.069787
 
7.8%
0.0716570
 
13.2%
0.087001
 
5.6%
0.092569
 
2.0%
ValueCountFrequency (%)
12139
1.7%
0.9931
 
< 0.1%
0.9835
 
< 0.1%
0.9786
 
0.1%
0.9663
 
0.1%
0.9587
 
0.1%
0.9445
 
< 0.1%
0.9354
 
< 0.1%
0.9240
 
< 0.1%
0.9186
 
0.1%

dst_host_same_src_port_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1483786873
Minimum0
Maximum1
Zeros63023
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:57.519906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.3089983508
Coefficient of variation (CV)2.08249821
Kurtosis2.76236142
Mean0.1483786873
Median Absolute Deviation (MAD)0
Skewness2.087032943
Sum18691.56
Variance0.09547998081
MonotonicityNot monotonic
2022-11-07T00:37:57.630893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063023
50.0%
0.0117657
 
14.0%
110307
 
8.2%
0.025743
 
4.6%
0.033278
 
2.6%
0.042096
 
1.7%
0.051664
 
1.3%
0.061299
 
1.0%
0.081086
 
0.9%
0.51077
 
0.9%
Other values (91)18742
 
14.9%
ValueCountFrequency (%)
063023
50.0%
0.0117657
 
14.0%
0.025743
 
4.6%
0.033278
 
2.6%
0.042096
 
1.7%
0.051664
 
1.3%
0.061299
 
1.0%
0.071051
 
0.8%
0.081086
 
0.9%
0.09712
 
0.6%
ValueCountFrequency (%)
110307
8.2%
0.99139
 
0.1%
0.98192
 
0.2%
0.97145
 
0.1%
0.96229
 
0.2%
0.95220
 
0.2%
0.94113
 
0.1%
0.93159
 
0.1%
0.92124
 
0.1%
0.91149
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0325427079
Minimum0
Maximum1
Zeros86903
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:57.745904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.18
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.1125642143
Coefficient of variation (CV)3.458968893
Kurtosis35.77292827
Mean0.0325427079
Median Absolute Deviation (MAD)0
Skewness5.548151304
Sum4099.47
Variance0.01267070235
MonotonicityNot monotonic
2022-11-07T00:37:57.899837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086903
69.0%
0.027952
 
6.3%
0.017146
 
5.7%
0.034723
 
3.7%
0.044518
 
3.6%
0.053048
 
2.4%
0.51550
 
1.2%
0.061330
 
1.1%
0.071036
 
0.8%
0.25951
 
0.8%
Other values (65)6815
 
5.4%
ValueCountFrequency (%)
086903
69.0%
0.017146
 
5.7%
0.027952
 
6.3%
0.034723
 
3.7%
0.044518
 
3.6%
0.053048
 
2.4%
0.061330
 
1.1%
0.071036
 
0.8%
0.08488
 
0.4%
0.09414
 
0.3%
ValueCountFrequency (%)
1691
0.5%
0.972
 
< 0.1%
0.931
 
< 0.1%
0.881
 
< 0.1%
0.862
 
< 0.1%
0.832
 
< 0.1%
0.84
 
< 0.1%
0.781
 
< 0.1%
0.7517
 
< 0.1%
0.732
 
< 0.1%

dst_host_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2844547201
Minimum0
Maximum1
Zeros81385
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:58.063850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4447850937
Coefficient of variation (CV)1.563641108
Kurtosis-1.047019322
Mean0.2844547201
Median Absolute Deviation (MAD)0
Skewness0.9659400321
Sum35833.33
Variance0.1978337796
MonotonicityNot monotonic
2022-11-07T00:37:58.201838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081385
64.6%
133562
26.6%
0.013345
 
2.7%
0.021158
 
0.9%
0.03711
 
0.6%
0.09419
 
0.3%
0.08413
 
0.3%
0.04372
 
0.3%
0.99304
 
0.2%
0.05298
 
0.2%
Other values (91)4005
 
3.2%
ValueCountFrequency (%)
081385
64.6%
0.013345
 
2.7%
0.021158
 
0.9%
0.03711
 
0.6%
0.04372
 
0.3%
0.05298
 
0.2%
0.06174
 
0.1%
0.07197
 
0.2%
0.08413
 
0.3%
0.09419
 
0.3%
ValueCountFrequency (%)
133562
26.6%
0.99304
 
0.2%
0.98169
 
0.1%
0.97100
 
0.1%
0.96102
 
0.1%
0.9571
 
0.1%
0.9487
 
0.1%
0.9376
 
0.1%
0.9253
 
< 0.1%
0.9148
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2784867272
Minimum0
Maximum1
Zeros85359
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:58.330863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4456702021
Coefficient of variation (CV)1.600328341
Kurtosis-1.00801633
Mean0.2784867272
Median Absolute Deviation (MAD)0
Skewness0.991721361
Sum35081.53
Variance0.1986219291
MonotonicityNot monotonic
2022-11-07T00:37:58.443851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
085359
67.8%
134256
27.2%
0.013762
 
3.0%
0.02640
 
0.5%
0.03160
 
0.1%
0.04111
 
0.1%
0.5107
 
0.1%
0.0576
 
0.1%
0.0872
 
0.1%
0.0771
 
0.1%
Other values (90)1358
 
1.1%
ValueCountFrequency (%)
085359
67.8%
0.013762
 
3.0%
0.02640
 
0.5%
0.03160
 
0.1%
0.04111
 
0.1%
0.0576
 
0.1%
0.0653
 
< 0.1%
0.0771
 
0.1%
0.0872
 
0.1%
0.0957
 
< 0.1%
ValueCountFrequency (%)
134256
27.2%
0.9853
 
< 0.1%
0.9756
 
< 0.1%
0.9644
 
< 0.1%
0.9526
 
< 0.1%
0.9422
 
< 0.1%
0.9320
 
< 0.1%
0.9226
 
< 0.1%
0.9120
 
< 0.1%
0.915
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1188323596
Minimum0
Maximum1
Zeros103178
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:58.557851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3065586135
Coefficient of variation (CV)2.579757017
Kurtosis3.692684133
Mean0.1188323596
Median Absolute Deviation (MAD)0
Skewness2.347432672
Sum14969.55
Variance0.09397818348
MonotonicityNot monotonic
2022-11-07T00:37:58.666868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0103178
81.9%
110298
 
8.2%
0.011800
 
1.4%
0.021222
 
1.0%
0.03497
 
0.4%
0.05408
 
0.3%
0.04397
 
0.3%
0.91267
 
0.2%
0.92257
 
0.2%
0.89244
 
0.2%
Other values (91)7404
 
5.9%
ValueCountFrequency (%)
0103178
81.9%
0.011800
 
1.4%
0.021222
 
1.0%
0.03497
 
0.4%
0.04397
 
0.3%
0.05408
 
0.3%
0.06220
 
0.2%
0.07164
 
0.1%
0.08147
 
0.1%
0.09104
 
0.1%
ValueCountFrequency (%)
110298
8.2%
0.9952
 
< 0.1%
0.9868
 
0.1%
0.97106
 
0.1%
0.96168
 
0.1%
0.95123
 
0.1%
0.94135
 
0.1%
0.93111
 
0.1%
0.92257
 
0.2%
0.91267
 
0.2%

dst_host_srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1202408472
Minimum0
Maximum1
Zeros106615
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:58.785849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3194604788
Coefficient of variation (CV)2.656838224
Kurtosis3.520596682
Mean0.1202408472
Median Absolute Deviation (MAD)0
Skewness2.337912407
Sum15146.98
Variance0.1020549975
MonotonicityNot monotonic
2022-11-07T00:37:58.944843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0106615
84.6%
113231
 
10.5%
0.011390
 
1.1%
0.02580
 
0.5%
0.03352
 
0.3%
0.05351
 
0.3%
0.04344
 
0.3%
0.98189
 
0.2%
0.99188
 
0.1%
0.06185
 
0.1%
Other values (91)2547
 
2.0%
ValueCountFrequency (%)
0106615
84.6%
0.011390
 
1.1%
0.02580
 
0.5%
0.03352
 
0.3%
0.04344
 
0.3%
0.05351
 
0.3%
0.06185
 
0.1%
0.0797
 
0.1%
0.0866
 
0.1%
0.0939
 
< 0.1%
ValueCountFrequency (%)
113231
10.5%
0.99188
 
0.1%
0.98189
 
0.2%
0.97103
 
0.1%
0.9678
 
0.1%
0.9573
 
0.1%
0.9475
 
0.1%
0.9350
 
< 0.1%
0.9238
 
< 0.1%
0.9151
 
< 0.1%

attack
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
normal
67342 
neptune
41214 
satan
 
3633
ipsweep
 
3599
portsweep
 
2931
Other values (18)
7253 

Length

Max length15
Median length6
Mean length6.38699076
Min length3

Characters and Unicode

Total characters804582
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowneptune
3rd rownormal
4th rownormal
5th rowneptune

Common Values

ValueCountFrequency (%)
normal67342
53.5%
neptune41214
32.7%
satan3633
 
2.9%
ipsweep3599
 
2.9%
portsweep2931
 
2.3%
smurf2646
 
2.1%
nmap1493
 
1.2%
back956
 
0.8%
teardrop892
 
0.7%
warezclient890
 
0.7%
Other values (13)376
 
0.3%

Length

2022-11-07T00:37:59.058849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal67342
53.5%
neptune41214
32.7%
satan3633
 
2.9%
ipsweep3599
 
2.9%
portsweep2931
 
2.3%
smurf2646
 
2.1%
nmap1493
 
1.2%
back956
 
0.8%
teardrop892
 
0.7%
warezclient890
 
0.7%
Other values (13)376
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n155804
19.4%
e98333
12.2%
a78970
9.8%
r75714
9.4%
m71528
8.9%
o71471
8.9%
l68308
8.5%
p56948
 
7.1%
t49623
 
6.2%
u43959
 
5.5%
Other values (14)33924
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter804491
> 99.9%
Connector Punctuation91
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n155804
19.4%
e98333
12.2%
a78970
9.8%
r75714
9.4%
m71528
8.9%
o71471
8.9%
l68308
8.5%
p56948
 
7.1%
t49623
 
6.2%
u43959
 
5.5%
Other values (13)33833
 
4.2%
Connector Punctuation
ValueCountFrequency (%)
_91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin804491
> 99.9%
Common91
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n155804
19.4%
e98333
12.2%
a78970
9.8%
r75714
9.4%
m71528
8.9%
o71471
8.9%
l68308
8.5%
p56948
 
7.1%
t49623
 
6.2%
u43959
 
5.5%
Other values (13)33833
 
4.2%
Common
ValueCountFrequency (%)
_91
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII804582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n155804
19.4%
e98333
12.2%
a78970
9.8%
r75714
9.4%
m71528
8.9%
o71471
8.9%
l68308
8.5%
p56948
 
7.1%
t49623
 
6.2%
u43959
 
5.5%
Other values (14)33924
 
4.2%

last_flag
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.50405646
Minimum0
Maximum21
Zeros66
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2022-11-07T00:37:59.147850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q118
median20
Q321
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.291511608
Coefficient of variation (CV)0.1174889754
Kurtosis13.36913405
Mean19.50405646
Median Absolute Deviation (MAD)1
Skewness-2.896759931
Sum2456965
Variance5.251025451
MonotonicityNot monotonic
2022-11-07T00:37:59.229849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2162557
49.7%
1820667
 
16.4%
2019338
 
15.4%
1910284
 
8.2%
153990
 
3.2%
173074
 
2.4%
162393
 
1.9%
12729
 
0.6%
14674
 
0.5%
11641
 
0.5%
Other values (12)1625
 
1.3%
ValueCountFrequency (%)
066
 
0.1%
162
 
< 0.1%
254
 
< 0.1%
365
 
0.1%
479
0.1%
581
0.1%
696
0.1%
7118
0.1%
8106
0.1%
9194
0.2%
ValueCountFrequency (%)
2162557
49.7%
2019338
 
15.4%
1910284
 
8.2%
1820667
 
16.4%
173074
 
2.4%
162393
 
1.9%
153990
 
3.2%
14674
 
0.5%
13451
 
0.4%
12729
 
0.6%

attack_class
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing1047
Missing (%)0.8%
Memory size984.3 KiB
0.0
67342 
1.0
45927 
2.0
11656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters374775
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.067342
53.5%
1.045927
36.5%
2.011656
 
9.3%
(Missing)1047
 
0.8%

Length

2022-11-07T00:37:59.326859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-07T00:37:59.421850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.067342
53.9%
1.045927
36.8%
2.011656
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0192267
51.3%
.124925
33.3%
145927
 
12.3%
211656
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number249850
66.7%
Other Punctuation124925
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0192267
77.0%
145927
 
18.4%
211656
 
4.7%
Other Punctuation
ValueCountFrequency (%)
.124925
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common374775
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0192267
51.3%
.124925
33.3%
145927
 
12.3%
211656
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII374775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0192267
51.3%
.124925
33.3%
145927
 
12.3%
211656
 
3.1%

Interactions

2022-11-07T00:37:43.665655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:02.279334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:06.125337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:09.693456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:13.266455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:16.891146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:20.262292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:23.994297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.506757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:31.378095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:34.967605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:38.380597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:42.064595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:45.722114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:49.244437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:52.711487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.550811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:00.117788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.633814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:07.047808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:10.983297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.506753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:18.030057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.618054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:25.229390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:28.894615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:32.907646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:36.479032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:40.079041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:43.781712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:02.425338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:06.236340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:09.815458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:13.383455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:17.008168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:20.375242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:24.124296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.617758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:31.514094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:35.079595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:38.491596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:42.173595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:45.833103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:49.357488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:52.827493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.661827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:00.232812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.750784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:07.153812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:11.100292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.626745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:18.150046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.732054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:25.362377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:29.006622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:33.027644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:36.587993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:40.190039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:43.908653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:02.551334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:06.366385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:09.940446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:13.517455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:17.128168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:20.498255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:24.254283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.743757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:31.655095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:35.197597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:38.618597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:42.291596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:45.953126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:49.483482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:52.950488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.798804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:00.354807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.881789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:07.275817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:11.219280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.755749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:18.282080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.855060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:25.481377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:29.132612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:33.154601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:36.705004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:40.308030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:44.032654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:02.699348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:06.491340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:10.060463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:13.651978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:17.241195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:20.621244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:24.383299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.863758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:31.798094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:35.323595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:38.739598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:42.412597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:46.080112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:49.616436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:53.072488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.917818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:00.474808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.997791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:07.400828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:11.341283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.881750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:18.407084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.977052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:25.598416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:29.252615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:33.280662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:36.820991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:40.430048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:44.157654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:02.859337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:06.612404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:10.309452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-07T00:37:24.575405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:28.246233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:32.294619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:35.864051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:39.465032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:43.030695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:46.630655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:05.368409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:09.195402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:12.778454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:16.441166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:19.806253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:23.496292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.026757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:30.567096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:34.504597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:37.895596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:41.618594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:45.256111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:48.766479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:52.221488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.086810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:59.613804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.152810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:06.567810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:10.516233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.020748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:17.539052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.135055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:24.701420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:28.387237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:32.422629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:35.982039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:39.594038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:43.153695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:46.744655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:05.770339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:09.317456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:12.900456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:16.554115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:19.923267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:23.623296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.146697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:30.964094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:34.622595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:38.016602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:41.729599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:45.373113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:48.888491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:52.339488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.201809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:59.736824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.277826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:06.685823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:10.629323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.145748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:17.665058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.261048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:24.829407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:28.514920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:32.546654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:36.107030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:39.715042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:43.283701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:46.856651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:05.888379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:09.439404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:13.024451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:16.667174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:20.040267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:23.745296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.261745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:31.095093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:34.739596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:38.133600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:41.841593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:45.493112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:49.011490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:52.456489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.316811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:59.870811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.399816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:06.805821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:10.750295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.268754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:17.787058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.382057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:24.961379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:28.645907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:32.668652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:36.230073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:39.835987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:43.409654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:46.972656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:06.015377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:09.571453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:13.146447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:16.782117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:20.154241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:23.874297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:27.385695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:31.233095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:34.854598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:38.255595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:41.955595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:45.610112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:49.133488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:52.584483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:56.431821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:36:59.990811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:03.520815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:06.935821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:10.868276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:14.393747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:17.912055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:21.507006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:25.093390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:28.777626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:32.786599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:36.352039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:39.961036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-07T00:37:43.539655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-07T00:37:59.551849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-07T00:37:59.936862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-07T00:38:00.312893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-07T00:38:00.669895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-07T00:38:01.009887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-07T00:38:01.219901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-07T00:37:47.220656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-07T00:37:48.624835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-07T00:37:49.578836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateattacklast_flagattack_class
00udpotherSF146000000000000000001310.00.00.00.00.080.150.0025510.000.600.880.000.000.000.00.00normal150.0
10tcpprivateS000000000000000000012361.01.00.00.00.050.070.00255260.100.050.000.001.001.000.00.00neptune191.0
20tcphttpSF23281530000010000000000550.20.20.00.01.000.000.00302551.000.000.030.040.030.010.00.01normal210.0
30tcphttpSF199420000001000000000030320.00.00.00.01.000.000.092552551.000.000.000.000.000.000.00.00normal210.0
40tcpprivateREJ000000000000000000121190.00.01.01.00.160.060.00255190.070.070.000.000.000.001.01.00neptune211.0
50tcpprivateS000000000000000000016691.01.00.00.00.050.060.0025590.040.050.000.001.001.000.00.00neptune211.0
60tcpprivateS0000000000000000000117161.01.00.00.00.140.060.00255150.060.070.000.001.001.000.00.00neptune211.0
70tcpremote_jobS0000000000000000000270231.01.00.00.00.090.050.00255230.090.050.000.001.001.000.00.00neptune211.0
80tcpprivateS000000000000000000013381.01.00.00.00.060.060.00255130.050.060.000.001.001.000.00.00neptune211.0
90tcpprivateREJ000000000000000000205120.00.01.01.00.060.060.00255120.050.070.000.000.000.001.01.00neptune211.0

Last rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateattacklast_flagattack_class
1259620tcphttpSF33416000000010000000000330.000.000.00.01.000.000.002552551.000.000.000.000.000.00.000.0normal210.0
1259630tcpprivateS000000000000000000012891.001.000.00.00.070.050.00255120.050.060.000.001.001.00.000.0neptune211.0
1259640tcpsmtpSF22333650000010000000000110.000.000.00.01.000.000.00121.000.001.001.000.000.00.000.0normal190.0
1259650tcpprivateS000000000000000000011331.001.000.00.00.030.070.00255130.050.070.000.001.001.00.000.0neptune211.0
1259660tcphttpSF35937500000100000000003110.330.090.00.01.000.000.1832551.000.000.330.040.330.00.000.0normal180.0
1259670tcpprivateS0000000000000000000184251.001.000.00.00.140.060.00255250.100.060.000.001.001.00.000.0neptune201.0
1259688udpprivateSF1051450000000000000000220.000.000.00.01.000.000.002552440.960.010.010.000.000.00.000.0normal210.0
1259690tcpsmtpSF22313840000010000000000110.000.000.00.01.000.000.00255300.120.060.000.000.720.00.010.0normal180.0
1259700tcpkloginS000000000000000000014481.001.000.00.00.060.050.0025580.030.050.000.001.001.00.000.0neptune201.0
1259710tcpftp_dataSF15100000010000000000110.000.000.00.01.000.000.00255770.300.030.300.000.000.00.000.0normal210.0